Active MR k-space Sampling with Reinforcement Learning
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
Abstract
Deep learning approaches have recently shown great promise in accelerating magnetic resonance image (MRI) acquisition. The majority of existing work have focused on designing better reconstruction models given a pre-determined acquisition trajectory, ignoring the question of trajectory optimization. In this paper, we focus on learning acquisition trajectories given a fixed image reconstruction model. We formulate the problem as a sequential decision process and propose the use of reinforcement learning to solve it. Experiments on a large scale public MRI dataset of knees show that our proposed models significantly outperform the state-of-the-art in active MRI acquisition, over a large range of acceleration factors.
Details
Original language | English |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings |
Editors | Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz |
Publisher | Springer, Berlin [u. a.] |
Pages | 23-33 |
Number of pages | 11 |
ISBN (print) | 9783030597122 |
Publication status | Published - 2020 |
Peer-reviewed | Yes |
Publication series
Series | Lecture Notes in Computer Science, Volume 12262 |
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ISSN | 0302-9743 |
Conference
Title | 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020 |
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Duration | 4 - 8 October 2020 |
City | Lima |
Country | Peru |
External IDs
ORCID | /0000-0001-9430-8433/work/146646291 |
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Keywords
ASJC Scopus subject areas
Keywords
- Active MRI acquisition, Reinforcement learning